ruocwang

ruocwang's Stars

• microsoft/Graphormer

  Graphormer is a general-purpose deep learning backbone for molecular modeling.

  Language:Python2.2k343

• davda54/sam

  SAM: Sharpness-Aware Minimization (PyTorch)

  Language:Python1.8k195

• divelab/DIG

  A library for graph deep learning research

  Language:Python1.9k284

• twitter-research/sign

  SIGN: Scalable Inception Graph Network

  Language:Python9514

• skepsun/SAGN_with_SLE

  Language:Python364

• VICO-UoE/DatasetCondensation

  Dataset Condensation (ICLR21 and ICML21)

  Language:Python49394

• hwwang55/GCN-LPA

  A tensorflow implementation of GCN-LPA

  Language:Python9923

• lukemelas/do-you-even-need-attention

  Is the attention layer even necessary? (https://arxiv.org/abs/2105.02723)

  Language:Python48332

• google-research/vision_transformer

  Language:Jupyter Notebook10.7k1.3k

• isaaccorley/mlp-mixer-pytorch

  PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021)

  Language:Python305

• rishikksh20/MLP-Mixer-pytorch

  Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision

  Language:Python21427

• DongHande/PT_propagation_then_training

  The official pytorch implementation of Propagation_then_Training for graph (https://arxiv.org/abs/2010.12408)

  Language:Python261

• acecreamu/ai-residency

  Example of a Cover letter for AI Residency

  795

• Chillee/ogb_baselines

  A couple of simple baselines for OGB.

  Language:Python31

• mengyangniu/ogbn-papers100m-sage

  Language:Python12

• ruocwang/darts-pt

  [ICLR2021 Outstanding Paper] Rethinking Architecture Selection in Differentiable NAS

  Language:Python10314

• PaddlePaddle/PGL

  Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

  Language:Python1.6k313

• CUAI/CorrectAndSmooth

  [ICLR 2021] Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (https://arxiv.org/abs/2010.13993)

  Language:Python28752

• romulus0914/NASBench-PyTorch

  A PyTorch implementation of NASBench

  Language:Python529

• Stanford-ILIAD/active-preference-based-gpr

  Companion code for RSS 2020 paper: "Active Preference-Based Gaussian Process Regression for Reward Learning"

  Language:Python409

• SamsungLabs/zero-cost-nas

  Zero-Cost Proxies for Lightweight NAS

  Language:Jupyter Notebook14320

• tkipf/pygcn

  Graph Convolutional Networks in PyTorch

  Language:Python5.2k1.2k

• benedekrozemberczki/ClusterGCN

  A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

  Language:Python790135

• wujian16/Cornell-MOE

  A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.

  Language:C++26664

• j-wilson/MaximizingAcquisitionFunctions

  Code for "Maximizing Acquisition Functions for Bayesian Optimization"

  Language:Jupyter Notebook123

• sebtsh/Top-k-Ranking-Bayesian-Optimization

  Language:Jupyter Notebook83

• haowei01/pytorch-examples

  train models in pytorch, Learn to Rank, Collaborative Filter, Heterogeneous Treatment Effect, Uplift Modeling, etc

  Language:Python17720

• yanshanjing/RankNet-Pytorch

  RankNet-Pytorch

  Language:Python256

• RuiShu/nn-bayesian-optimization

  We use a modified neural network instead of Gaussian process for Bayesian optimization.

  Language:Python10730

• AaltoPML/PPBO

  Projective Preferential Bayesian Optimization

  Language:Python18